{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import VFF\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.random.rand(100, 2)\n",
    "Y = np.sin(3 * X[:, :1]) * np.sin(5 * X[:, 1:]) + np.random.randn(X.shape[0], 1) * 0.1\n",
    "# Y = np.sin(8 * X[:, 1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1216f5e80>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QsYwpSO+BUl2WAuJTI18EDBWRgYQT+CWEc2ATEekHvARcoZRaF7U/EzCUUnWR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\n3BhjIu7/EHmafKiv4jYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e8512e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], 20, Y[:, 0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = VFF.gpr.GPRKron(X, Y, np.arange(18), np.array([-1, -1]), np.array([2, 2]), [GPflow.kernels.Matern12(1), GPflow.kernels.Matern12(1)])\n",
    "m.likelihood.variance = 0.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "k = GPflow.kernels.Matern32(1) * GPflow.kernels.Matern32(1, active_dims=[1]) + GPflow.kernels.White(1)\n",
    "m0 = GPflow.gpr.GPR(X, Y, kern=k)\n",
    "m0.likelihood.variance = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      fun: -45.779183835929516\n",
       " hess_inv: <6x6 LbfgsInvHessProduct with dtype=float64>\n",
       "      jac: array([ -4.16419685e-06,   6.66518484e-06,  -1.31283273e-05,\n",
       "         6.66518483e-06,   1.92558733e-07,   2.75248998e-06])\n",
       "  message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'\n",
       "     nfev: 18\n",
       "      nit: 14\n",
       "   status: 0\n",
       "  success: True\n",
       "        x: array([-0.5816852 , -0.65854351, -0.84389603, -0.65854351, -7.34731781,\n",
       "       -4.67885514])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.optimize()\n",
    "m0.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='params' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constraint</td></tr><tr><td>model.kerns.item0.lengthscales</td><td>[ 0.61889174]</td><td>None</td><td>+ve</td></tr><tr><td>model.kerns.item0.variance</td><td>[ 0.26679497]</td><td>None</td><td>+ve</td></tr><tr><td>model.kerns.item1.lengthscales</td><td>[ 0.40846014]</td><td>None</td><td>+ve</td></tr><tr><td>model.kerns.item1.variance</td><td>[ 0.26679497]</td><td>None</td><td>+ve</td></tr><tr><td>model.likelihood.variance</td><td>[ 0.01991182]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<VFF.gpr.GPRKron at 0x123c13860>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id='params' width=100%><tr><td>Name</td><td>values</td><td>prior</td><td>constraint</td></tr><tr><td>name.kern.prod.matern32_1.lengthscales</td><td>[ 0.44401712]</td><td>None</td><td>+ve</td></tr><tr><td>name.kern.prod.matern32_1.variance</td><td>[ 0.41713422]</td><td>None</td><td>+ve</td></tr><tr><td>name.kern.prod.matern32_2.lengthscales</td><td>[ 0.35769771]</td><td>None</td><td>+ve</td></tr><tr><td>name.kern.prod.matern32_2.variance</td><td>[ 0.41713422]</td><td>None</td><td>+ve</td></tr><tr><td>name.kern.white.variance</td><td>[ 0.00064511]</td><td>None</td><td>+ve</td></tr><tr><td>name.likelihood.variance</td><td>[ 0.00924776]</td><td>None</td><td>+ve</td></tr></table>"
      ],
      "text/plain": [
       "<GPflow.gpr.GPR at 0x123c1b908>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1255879e8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u3Kaf9ZWlrCgrZulF15Pi6PlSW5qmhUCBClOvlWgTStVKS+d3EXES6Py+oN05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\n/g8SoeKZOFo3IQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be69160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xtest = np.random.rand(100,2)\n",
    "mu, var = m0.predict_f(Xtest)\n",
    "plt.scatter(Xtest[:, 0], Xtest[:, 1], 20, mu[:, 0], vmin=-1, vmax=1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x128308b00>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qWLSrjHcuuIZ0Z/eX2tI0LQIKVJR6rcSbSKpWmju/i4iLUOf32W3OeQU4SUQc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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125597ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu, var = m.predict_f(Xtest)\n",
    "plt.scatter(Xtest[:, 0], Xtest[:, 1], 20, mu[:, 0], vmin=-1, vmax=1)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
